In this work, we considered as a starting point a model of yolo pre-trained on the COCO dataset [1]}, a large collection of images describing complex everyday scenes of common objects in their natural context, categorized in 80 different categories. Since this network is a generic objects detector, we specialized it to localize and recognize object instances belonging to only a specific category - i.e., the car category in our case. Specifically, we extracted the weights of the first 81 layers of this pre-trained model since these layers capture universal features (like curves and edges), which are also relevant to our task. Then, we re-trained the detector by initialing the first 81 layers with the previously extracted weights and the weights associated with the remaining layers at random, exploiting annotated data describing our specific scenario for the supervised learning. Thus, we got the network to focus on learning dataset-specific features in the last layers. Furthermore, in such a way, we reduced the dimension of the predicted output tensors, from \(S_i \times S_i \times (B \times (80 + 5)) = S_i \times S_i \times 255\)  to \(S_i \times S_i \times (B \times (1 + 5)) = S_i \times S_i \times 18)\) , where \(S_i\)  are the three different grids exploited by yolo, saving memory usage and making our solution more suitable for the limited computational capabilities of the embedded vision system employed in our testing scenario.
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